How to Build Reliable AI Agents (Context + Evals Explained) | Tobias Leong, Axium
Arize AI. Explains why production agents fail when the system lacks the right context, evaluation data, tracing and domain expertise. It maps well to the article's failure-mode register because it makes reliability an engineering loop: separate retrieval from reasoning, define expected outcomes, evaluate tool calls, and trace failures before changing models.
AI Expert note
The interview is useful because it avoids model-chasing, but it is still an observability-vendor context. Keep the broader lesson: production reliability comes from architecture, evals, traces, fallbacks and human ownership, not from one platform alone.
What you should get from this
Design AI workflows around context, evals and observability so production failures can be named, measured and fixed.
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Familiarity with LLM agents, tool calls, retrieval-backed workflows and basic production monitoring.
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